Optimize · Pricing / Offer Tests

Test a Performance-Based Pricing Pitch

Design a controlled test of a performance-based pricing pitch against your current fixed-fee offer.

foundermanagerrepAdvanced⏱ 5-8 hours of pricing model work
When to use
Use when prospects keep asking for skin-in-the-game pricing, or when your fixed-fee close rate is stalling on price-sensitive segments. Only run this if you have reliable attribution and can stomach 60-90 days of variable revenue while you learn.
The prompt
You are an agency monetization strategist who runs pricing tests responsibly (no race to the bottom). You design experiments with explicit hypotheses, clear primary metrics, and pre-committed kill criteria so the agency learns regardless of outcome.
Agency: [AGENCY_NAME] — [SERVICES] | Current offer: [CURRENT_OFFER] @ [CURRENT_PRICE] | Win rate: [WIN_RATE] | Avg deal: [AVG_DEAL_SIZE] | Test audience: [TEST_AUDIENCE] | Hypothesis: [HYPOTHESIS] | Performance KPI: [PERFORMANCE_KPI] | Attribution quality: [ATTRIBUTION_QUALITY]
Design a controlled test of a performance-based pricing variant (base + performance bonus on [PERFORMANCE_KPI]) against the current fixed-fee offer, including blended-margin math and KPI gates.

- Variant must have a floor (base fee) ≥ delivery cost
- Define what counts as "performance" with measurement source and verification cadence
- Show blended-margin math at pessimistic / realistic / optimistic outcomes
- Include explicit kill criteria for attribution disputes
- Specify which client profiles are eligible (and which are excluded — e.g. broken funnel)

Sections: (1) Variant table (Control vs Perf), (2) Performance Definition + Measurement, (3) Margin Scenarios table, (4) Eligibility Filter, (5) Primary Metric + Decision Rule, (6) Kill Criteria.
Variables
  • [AGENCY_NAME] — Your agency name
  • [SERVICES] — Core services
  • [CURRENT_OFFER] — Current fixed-fee offer
  • [CURRENT_PRICE] — Current monthly price
  • [WIN_RATE] — Current close rate %
  • [AVG_DEAL_SIZE] — Current ACV
  • [TEST_AUDIENCE] — Segment to test against
  • [HYPOTHESIS] — What you expect to happen
  • [PERFORMANCE_KPI] — The KPI the bonus is tied to (e.g. qualified leads, MQLs, revenue lift)
  • [ATTRIBUTION_QUALITY] — How clean attribution is (rated low/med/high)
Example input
Agency: Anchor Growth — paid social for ecom DTC | Current offer: $6k/mo management + ad spend | Win rate: 19% | Avg deal: $72k | Test audience: $2-10M GMV DTC brands | Hypothesis: a perf component lifts close rate 8pts on prospects who balk at fixed fee | Performance KPI: incremental new-customer revenue (GA4 + Shopify) | Attribution quality: medium
Example output
1) Variants:
| Variant | Structure | Price |
|---|---|---|
| Control | Fixed mgmt | $6k/mo |
| Perf | $4k base + 8% of incremental new-cust revenue | floor $4k |

2) Performance Def: incremental = month-over-month new-customer revenue lift above 3-mo pre-engagement baseline; verified monthly via shared GA4 + Shopify dashboard; disputes resolved within 7 days.

3) Margins:
| Scenario | Blended Fee | Margin |
|---|---|---|
| Pessimistic (0 lift) | $4k | 25% |
| Realistic ($60k lift) | $8.8k | 58% |
| Optimistic ($120k lift) | $13.6k | 71% |

4) Eligible: clean GA4, 6+ mo Shopify history, >$150k/mo GMV. Excluded: subscription-only, attribution gaps.

5) Primary: ACV per pitch. Decision: adopt if Perf ACV/pitch >= Control by 15% over 30 pitches.

6) Kill: 2+ attribution disputes in 60 days, or blended margin <40% across cohort.
Pro tips
  • Never offer pure performance pricing — always set a base that covers delivery cost
  • Pre-agree the measurement source in the contract; attribution fights kill margin faster than discounts
  • Filter eligibility ruthlessly — clients with broken funnels make perf pricing a liability
Works with
ClaudeChatGPTGemini
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